R Notebook

library(sf)

## Linking to GEOS 3.4.2, GDAL 2.1.2, proj.4 4.9.1

library(ggplot2) #development version!
## devtools::install_github("tidyverse/ggplot2")
library(tidyverse)

## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr

## Conflicts with tidy packages ----------------------------------------------

## filter(): dplyr, stats
## lag():    dplyr, stats

library(readr)
library(cowplot)

## 
## Attaching package: 'cowplot'

## The following object is masked from 'package:ggplot2':
## 
##     ggsave

library(sp)
library(gridExtra)

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:dplyr':
## 
##     combine

library(dplyr)

Import Data

Now I import my data. I filter for the Arran postcodes, (since Arran all begins ‘KA27’).

## Finding the Arran coordinates
allcoordinates <- read.csv("alldata/ukpostcodes.csv")
arrancoordinates <- filter(allcoordinates,substr(postcode,1,4)=="KA27")

ggplot(data = arrancoordinates) +
  geom_point(mapping = aes(x = longitude, y = latitude), shape=20) +
  theme(plot.title = element_text(hjust = 0.5)) +
  theme_grey() +
  coord_map()

Now I create some plots. #Arran Borders

pcs <- read_sf("alldata/Scotland_pcs_2011")

#Print Post codes lists
arransubsect <- filter(pcs,substr(label,1,4)=="KA27")

#Simple.sf
#After a little editing I can overlay the two.

simple.sf <- st_as_sf(arrancoordinates, coords=c('longitude','latitude'))
st_crs(simple.sf) <- 4326

plot1 <- simple.sf %>% ggplot() + geom_sf(shape=20) +
theme_grey() +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank())

plot2 <- arransubsect %>%
  ggplot() +
  geom_sf() +
  theme_grey() +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  geom_sf(data=simple.sf, shape=20)

Shape files

Now I load the SIMD data, containing the geometries (shapefiles) and SIMD data (percentiles, etc)

#Import SIMD data from http://www.gov.scot/Topics/Statistics/SIMD
#https://data.gov.uk/dataset/scottish-index-of-multiple-deprivation-simd-2012
#https://data.gov.uk/dataset/scottish-index-of-multiple-deprivation-simd-2012/resource/d6fa8924-83da-4e80-a560-4ef0477f230b
DZBoundaries2016 <- read_sf("./alldata/SG_SIMD_2016")
DZBoundaries2012 <- read_sf("./alldata/SG_SIMD_2012")
DZBoundaries2009 <- read_sf("./alldata/SG_SIMD_2009")
DZBoundaries2006 <- read_sf("./alldata/SG_SIMD_2006")
DZBoundaries2004 <- read_sf("./alldata/SG_SIMD_2004")

Select Arran SIMD data

I have to choose the right columns manually in order to select the Arran data.

#Selecting Arran data from Scotland (2016)
#Find postcode look-up from below file for KA27 postcodes. Find unique DZ. Find row positions.
#SIMD2016 <-read.csv("./alldata/00505244.csv")
#Selecting ArranDZ2016
Arrandz2016 <- c(4672,4666,4669,4671,4667,4668,4670)
arran2016 <- DZBoundaries2016[Arrandz2016,]

#Find postcode look-up, KA27 postcodes. Find unique DZ. Find row positions.
#Selecting ArranDZ2012
Arrandz2012 <- c(4409,4372,4353,4352,4351,4350,4349)

#2012
arran2012 <- DZBoundaries2012[Arrandz2012,]
#2009
arran2009 <- DZBoundaries2009[Arrandz2012,]
#2006
arran2006 <- DZBoundaries2006[Arrandz2012,]
#2004
arran2004 <- DZBoundaries2004[Arrandz2012,]

Now I want to plot all the data, first I combine it all into one table. First I subselect the data I want from the appropriate columns.

arran20162 <- arran2016 %>%
  select(DataZone, geometry, Percentile)  %>%
  mutate(year="2016")

arran20122 <- arran2012 %>%
  select(DataZone, geometry, Percentile) %>%
  mutate(year="2012")

arran20092 <- arran2009 %>%
  select(DataZone, geometry, Percentile) %>%
  mutate(year="2009")

arran20062 <- arran2006 %>%
  select(DataZone, geometry, Percentile) %>%
  mutate(year="2006")

arran20042 <- arran2004 %>%
  select(DataZone, geometry, Percentile) %>%
  mutate(year="2004")

#Now I add it together
arransimd <- rbind(arran20162,arran20122,arran20092,arran20062,arran20042)

The reason I’ve downloaded all the datazones shapefiles individually is because they change between 2016 and 2012. See the small differences.

arran1612 <- rbind(arran20162,arran20122)

arran1612 %>%
  ggplot() +
  geom_sf(aes(fill = DataZone)) +
  facet_wrap('year') +
  theme_grey() +
  theme(legend.position="none") +
  theme(axis.text.x=element_text(angle=45, hjust = 1))

plot6 <- arran2016 %>%
  ggplot() +
  geom_sf() +
  theme_grey() +
  theme(legend.position="none") +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank())

plot7 <- arran2016 %>%
  ggplot() +
  geom_sf(aes(fill = DataZone)) +
  theme_grey() +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank())

plot8 <- arran2016 %>%
  ggplot() +
  geom_sf(aes(fill = Percentile)) +
  theme_grey() +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank())

grid.arrange(plot6, plot7, plot8, nrow = 1)

Arran Percentile Plots

Now I plot the percentiles.

arransimd %>%
  ggplot() +
  geom_sf(aes(fill = Percentile)) +
  facet_wrap('year', nrow = 1) +
  theme_grey() +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank(), axis.text.y=element_blank(),
        axis.ticks.y=element_blank())

There we are. The SIMD health percentiles of Arran zones throughout SIMD history. And I’ve learned a little bit about graphics in R.

If I wanted to I could show the zones individually.. First I find the unique zones. (There are 14. 7 Zones 2016, 7 Zones pre-2016)

datazones <- unique(arransimd$DataZone)

I’ll have to find out a simpler way to do this but.. In order to turn the names into arguments I’ve made a function that filters the data into an individual name. #Pre-2016 Individual Zones

function0.5 <- function(argument) 
{
  filter(arransimd, DataZone==argument)
}

So by reading ‘datazones’ I’ve made a list of the output

#all datazones
datazonelist <- lapply(datazones, function0.5)

#Pre-2016 lists
pre2016list2 <- list("S01004409", "S01004372", "S01004353", "S01004352", "S01004351", "S01004350", "S01004349")
#Create a new way of making character list
pre2016list <- lapply(pre2016list2, function0.5)

#Post-2016 Lists
post2016list2 <- list("S01011177", "S01011171", "S01011174", "S01011176", "S01011172", "S01011173", "S01011175")
post2016list <- lapply(post2016list2, function0.5)

function1 <- function(argument) 
{
  argument %>%
  ggplot() +
  geom_sf(aes(fill = Percentile)) +
  facet_wrap('year') +
  theme_grey() +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank())
}

function2 <- function(argument) 
{
  arransubsect %>%
  ggplot() +
  geom_sf() +
  theme_grey() +
  theme(axis.text.x=element_text(angle=45, hjust = 1)) +
  geom_sf(data= argument, aes(fill = DataZone))
}

function3 <- function(argument) 
{
  argument %>%
  ggplot() +
  geom_sf(data = arransubsect) +
  geom_sf(aes(fill = Percentile)) +
  facet_wrap('year') +
  theme_grey() +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank())
}

function4 <- function(argument) 
{
  plot_grid((function1(argument)), (function2(argument)), labels = c("A", "B"))
}

Now I want to overlay the postcodes for a particular shapefile, in this case by Datazone. To do this I’ve converted both the Arran coordinates and Arran (2016) shapefiles into Spatial Points/Polygons, converted them into a common CRS, and then compared them by using over().

exampleshapes <- sf:::as_Spatial(arran2016$geom)
examplepoints <- sf:::as_Spatial(simple.sf$geom)

examplepoints <- spTransform(examplepoints, CRS("+proj=longlat +datum=WGS84"))
exampleshapes <- spTransform(exampleshapes, CRS("+proj=longlat +datum=WGS84"))

namingdzpostcode <- over(exampleshapes, examplepoints, returnList = TRUE)

I can then take a member reference from the orginal postcode list, which gives me a selection of the rows in that DZ. For simplicity I’ve written this as a new function.

Unfortunately, I haven’t worked out how to coordinate the new ID with the original DZ names yet, so I have to select by using the appropriate ID for each DZ.

pre2016listID <- list(3,2,1,4,7,6,5)
post2016listID <- list(1,2,3,4,5,6,7)

Function selecting simple.sf by DZ ID.

function6 <- function(argument) 
{
  simple.sf[namingdzpostcode[[argument]],]
}

plot3 <- arran2016 %>%
  ggplot() +
  geom_sf() +
  theme_grey() +
  theme(legend.position="none") +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  geom_sf(data=simple.sf, shape=20)

plot4 <-   arransubsect %>%
  ggplot() +
  geom_sf() +
  theme_grey() +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  theme(legend.position="none") +
  geom_sf(data= function0.5("S01004372"), aes()) +
  geom_sf(data=function6(2), shape=20)

plot5 <- function0.5("S01004372") %>%
  ggplot() +
  geom_sf() +
  theme_grey() +
  geom_sf(data=function6(2), shape=20) +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank())

grid.arrange(plot1, plot2, plot3, plot4, plot5, nrow = 1)

If I edit function 4 a little so that the geom_sf layer is a second argument then I can also use function 4.

function4.5 <- function(argument, argument2) 
{
  a <- function1(argument)
  b <- function2(argument) +
  geom_sf(data=function6(argument2), shape=20)

  plot_grid(a, b, labels = c("A", "B"))
}

I’ve also made another function to plot the DZ on it’s own with coordinates.

function5 <- function(argument, argument2) 
{
  argument %>%
  ggplot() +
  geom_sf() +
  theme_grey() +
  geom_sf(data=function6(argument2), shape=20) +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank())
}

function1.5 <- function(argument) 
{
  argument %>%
  ggplot() +
  geom_sf(aes(fill = Percentile)) +
  facet_wrap('year') +
  theme_grey() +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  theme(legend.position="bottom")  
}

function2.5 <- function(argument) 
{
  arransubsect %>%
  ggplot() +
  geom_sf() +
  theme_grey() +
  theme(axis.text.x=element_text(angle=45, hjust = 1)) +
  theme(legend.position="none") +
  geom_sf(data= argument, aes(fill = DataZone))
}

function7.5 <- function(argument, argument2) 
{
  a <- function1.5(argument)
  b <- function2.5(argument) 
  c <- function5(argument, argument2)
  grid.arrange(a, b, c, nrow = 1)
}

function2.5.1 <- function(argument) 
{
  arransubsect %>%
  ggplot() +
  geom_sf() +
  theme_grey() +
  theme(axis.text.x=element_text(angle=45, hjust = 1)) +
  theme(legend.position="bottom") +
  geom_sf(data= argument, aes(fill = DataZone))
}

function7.5.1 <- function(argument, argument2) 
{
  a <- function1.5(argument)
  b <- function2.5.1(argument) 
  c <- function5(argument, argument2)
  grid.arrange(a, b, c, nrow = 1)
}

Pre-2016

function7.5.1.2 <- function(argument)
{
  function7.5.1(pre2016list[[argument]],pre2016listID[[argument]])
}

lapply(1:7, function7.5.1.2)

## [[1]]
## TableGrob (1 x 3) "arrange": 3 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 
## [[2]]
## TableGrob (1 x 3) "arrange": 3 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 
## [[3]]
## TableGrob (1 x 3) "arrange": 3 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 
## [[4]]
## TableGrob (1 x 3) "arrange": 3 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 
## [[5]]
## TableGrob (1 x 3) "arrange": 3 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 
## [[6]]
## TableGrob (1 x 3) "arrange": 3 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 
## [[7]]
## TableGrob (1 x 3) "arrange": 3 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]

Post-2016

function7.5.1.1 <- function(argument)
{
  function7.5.1(post2016list[[argument]],post2016listID[[argument]])
}

lapply(1:7, function7.5.1.1)

## [[1]]
## TableGrob (1 x 3) "arrange": 3 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 
## [[2]]
## TableGrob (1 x 3) "arrange": 3 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 
## [[3]]
## TableGrob (1 x 3) "arrange": 3 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 
## [[4]]
## TableGrob (1 x 3) "arrange": 3 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 
## [[5]]
## TableGrob (1 x 3) "arrange": 3 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 
## [[6]]
## TableGrob (1 x 3) "arrange": 3 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 
## [[7]]
## TableGrob (1 x 3) "arrange": 3 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]

Annotate percentile!

plot text of percentile, at centre of shape file coordinates. overlay postcode labels.

overlay info onto leaflet then with labels.